Datasets.
For low resolution experiments, we train and evaluate on CIFAR10, ImageNet32/64. For high resolution experiments, we train our model on 50000 images from Open Image Train Dataset[1]} which are randomly selected and preprocessed as [2]} to prevent overfitting on JPEG artifacts. Then, we evaluate on natural high resolution datasets, CLIC.mobile, CLIC.pro[3]} and DIV2K[4]} to test the model generalization ability. For
high resolution setting, we crop 32 \(\times \)  32 patches for evaluating.
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